Machine learning
Many users of the post-industrial era wondered: What is Machine Learning? A fantastic future that has already arrived or another incomprehensible theory like quantum dualism. Neither one nor the other.
Machine learning (ML), as the term is translated, is a branch of artificial intelligence. In more detail, it is a data analysis technique that allows a machine / robot / analytical system to independently learn by solving an array of similar problems.
It looks a little cumbersome. To simplify, machine learning technology is a search for patterns in the array of information presented and the choice of the best solution without human intervention.
The principle of ML is interestingly demonstrated in the Google's DeepMind AI Just Taught Itself To Walk.
The analytical system was tasked with getting from one point to another using a two-legged and four-legged model. At the same time, they did not show what walking and movement on four limbs looks like. The machine, by going through the data array, making mistakes and trying again, found the optimal motion options for the two models.
As for the fantastic future, MO is conventionally divided into three stages of implementation:
- Technologies receive the prefix "innovative", which means that only large corporations and government agencies have access to them. For example, Google and Amazon, IBM and Apple were the first to introduce artificial intelligence. Actually, any system that tries to predict customer demand based on a massive amount of data is associated with machine learning technology.
- Technologies are used by people with a certain knowledge base in the IT field, who have access to modern developments and gadgets. The emergence of new services based on artificial intelligence technology. A striking example is Google and Yandex analytical engines in contextual advertising.
- Technologies are available even to schoolchildren, people of the "tube" generation, who quite seriously fear the "Rise of the Machines" by analogy with the blockbuster "Terminator".
Many experts believe that artificial intelligence is at a transitional stage between Level 2 and Level 3. That is, IT-savvy people are already using innovations, and most are still afraid.
Scope of application
We looked at Machine Learning - what this concept means. Now is the time to consider what ML is used for in business and life.
Ask a robotics enthusiast about the scope of machine learning. You will hear many fantastic stories. For example, robots will independently learn to perform tasks assigned by humans. To extract minerals in the bowels of the Earth, drill oil and gas wells, explore the depths of the ocean, extinguish fires, and more. The programmer will not need to write massive and complex programs for fear of making mistakes in the code. The robot, thanks to the MO, will itself learn to behave in a specific situation based on data analysis.
Great, but fantastic so far. In the future, maybe not even too distant, it will become a reality.
What artificial intelligence and machine learning are now capable of. Today technology is used more for marketing purposes. For example, Google and Yandex use ML to serve relevant ads to users. You have noticed more than once that after searching the Internet for a product of interest, then for several hours, or even days, they show you similar offers.
Smart feeds in social networks are formed on the same principle. Analytical machines FB, VK, Instagram, Twitter investigate your interests - which posts you view more often, what you click on, which publics or groups you visit, and more. The longer and more often you are active on social networks, the more personalized your news feed becomes. This is both good and bad. On the one hand, the machine filters out an array of uninteresting (in its opinion) information, and on the other, it narrows your horizons. Marketing is nothing personal!
Machine learning is used in security frameworks. For example, a subway facial recognition system. Cameras scan the faces of people entering and exiting the metro. Analytical engines compare images with persons who are on the wanted list. If the similarity is high, then the system beeps. Police officers go to check the documents of a particular person.
Artificial intelligence is already being implemented in medical institutions. For example, the processing of patient data, preliminary diagnostics and even the selection of individual treatment based on information about a person's disease.
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